About Upstart
At Upstart, we’re united by a mission that matters: to radically reduce the cost and complexity of borrowing for all Americans. Every day, we bring creativity, experimentation, and advanced AI to reshape access to credit, helping millions move forward financially with clarity and confidence.
As the leading AI lending marketplace, we partner with banks and credit unions to expand access to affordable credit through technology that’s both radically intelligent and deeply human. Our platform runs over one million predictions per borrower using more than 3,000 signals, powering smarter, fairer decisions for millions of customers. But the numbers only hint at the impact. Every idea, every voice, and every contribution moves us closer to a world where credit never stands between people and their financial progress.
We’re proudly digital-first, giving most Upstarters the flexibility to do their best work from wherever they thrive, alongside teammates across 80+ cities in the US and Canada. Digital-first doesn’t mean distant. We’re intentional about in-person connection through team onsites, planning sessions, and moments that spark creativity and trust. And whether you choose to work primarily from home or collaborate in-person from one of our offices in Columbus, Austin, the Bay Area, or New York City, you’ll have the support to work in the way that works best for you.
If you’re energized by tackling meaningful problems, excited to innovate with purpose, and motivated by work that truly matters, we’d love to hear from you.
The Team
Upstart’s Partnerships team helps drive customer acquisition and originations through partner offers. The team is focused on improving how offers are optimized across the customer journey.
As the Applied Scientist on the Partnerships team, you’ll use causal inference, experimentation, and machine learning to help determine which offers are most likely to support customer conversion. You’ll take on a greenfield optimization opportunity, moving from research and measurement to production-ready models and decision strategies.
How you’ll make an impact
- Develop causal machine-learning models to optimize customer offers at different points in the funnel, from marketing-time decisions with limited information to decisions informed by customer activity on our site.
- Analyze and interpret complex data, identify gaps in what we know, and design experiments to gather evidence for building and evaluating optimization models.
- Measure the impact of offer changes with statistically rigorous experiments and evaluations.
- Translate research and results into clear recommendations, decision strategies, and next steps for business and technical partners.
- Build production-ready models and code for use in offer selection.
- Partner with Partnerships leadership, Product, Engineering, peer machine-learning teams, and model-risk partners to align on strategy, implementation, and methodology.
Minimum Qualifications
- Graduate degree in mathematics, applied mathematics, statistics, physics, econometrics, operations research, computer science, or a related quantitative field.
- 2+ years of experience conducting machine-learning, statistical-modeling, or applied quantitative research in an academic or industry setting.
- Experience applying causal inference methods and experimentation to real-world decisions in an applied-science or data-science context.
- Applied statistics, probability, and experimental-design expertise used to build or evaluate models or interventions.
- Production-quality coding experience in Python, including data manipulation and algorithm implementation.
Preferred Qualifications
- PhD in a quantitative field.
- Experience with marketplace optimization, particularly where pricing or offer decisions are central.
- Experience with causal machine-learning methods or treatment-effect modeling.
- Experience with supervised learning, model evaluation, and feature engineering, especially with large or complex datasets.
- Experience taking work from exploratory analysis and experimentation through production-model development while maintaining analytical rigor and model reliability.
- Ability to translate broad business problems into structured research questions and communicate complex findings as actionable recommendations.
Position location This role is available in the following locations: Remote
Travel requirements As a digital first company, the majority of your work can be accomplished remotely. The majority of our employees can live and work anywhere in the U.S but are encouraged to to still spend high quality time in-person collaborating via regular onsites. The in-person sessions’ cadence varies depending on the team and role; most teams meet once or twice per quarter for 2-4 consecutive days at a time.
#LI-REMOTE
#LI-Associate
At Upstart, your base pay is one part of your total compensation package. The anticipated base salary for this position is expected to be within the below range. Your actual base pay will depend on your geographic location–with our “digital first” philosophy, Upstart uses compensation regions that vary depending on location. Individual pay is also determined by job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
In addition, Upstart provides employees with target bonuses, equity compensation, and generous benefits packages (including medical, dental, vision, and 401k).
United States | Remote – Anticipated Base Salary Range
$141,500—$196,000 USD
What you’ll love
At Upstart, our benefits for full-time employees are designed to support your health, financial well-being, family, and personal growth. Here’s what you can expect:
- Competitive compensation, including base pay, bonus opportunities, and annual equity grants that vest quarterly
- Retirement benefits to help you plan for the future, including a 401(k) or Group Retirement Savings Plan with a company match of $2 for every $1 contributed, up to $15,000 annually (USD in the US, CAD in Canada)
- Employee Stock Purchase Plan (ESPP) with discounted stock purchase options for eligible employees (US only)
- Comprehensive health coverage designed to support you and your family, including medical, dental, vision, and wellness resources for US and supplemental health coverage for Canada.
- Health Savings Account contributions from Upstart for eligible plans (US only)
- Income protection benefits, including life insurance and disability coverage for added financial security
- Paid time off, sick leave, and company holidays, in line with local requirements
- Paid family and parental leave to support caregiving and major life moments (duration varies by country)
- Family-centered benefits to support fertility, parenthood, and caregiving needs
- Employee Assistance Program (EAP) offering mental health support and life-centered resources
- Financial wellness resources, including access to financial planning tools and a financial concierge service (US Only)
- Annual wellness allowance to support your physical and emotional well-being and personal development, based on what matters most to you
- Annual productivity allowance to invest in relevant tools and resources you need to do your best work, no matter where you work from
- Connection and community through team events, all-company updates, and employee resource groups (ERGs)
- Onsite perks, including catered lunches and fully stocked micro-kitchens when working from one of our offices in the Bay Area, Austin, Columbus, and New York City
For roles based in Canada, please note that we are not currently able to hire in Quebec.
Upstart is a proud Equal Opportunity Employer. Just as we are dedicated to improving access to affordable credit for all, we are committed to inclusive and fair hiring practices.
If you require reasonable accommodation in completing an application, interviewing, completing any pre-employment testing, or otherwise participating in the employee selection process, please email candidate_accommodations@upstart.com
https://www.upstart.com/candidate_privacy_policy
